Exploiting growing stock volume maps for large scale forest resource assessment: Cross-comparisons of ASAR- and PALSAR-based GSV estimates with forest inventory in Central Siberia

Growing stock volume is an important biophysical parameter describing the state and dynamics of the Boreal zone. Validation of growing stock volume (GSV) maps based on satellite remote sensing is challenging due to the lack of consistent ground reference data. The monitoring and assessment of the remote Russian forest resources of Siberia can only be done by integrating remote sensing techniques and interdisciplinary collaboration. In this paper, we assess the information content of GSV estimates in Central Siberian forests obtained at 25m from ALOS-PALSAR and 1km from ENVISAT-ASAR backscatter data. The estimates have been cross-compared with respect to forest inventory data showing 34% relative RMSE for the ASAR-based GSV retrievals and 39.4% for the PALSAR-based estimates of GSV. Fragmentation analyses using a MODIS-based land cover dataset revealed an increase of retrieval error with increasing fragmentation of the landscape. Cross-comparisons of multiple SAR-based GSV estimates helped to detect inconsistencies in the forest inventory data and can support an update of outdated forest inventory stands

A Modelling System for Dead Wood Assessment in the Forests of Northern Eurasia

Dead wood, including coarse woody debris, CWD, and fine woody debris, FWD, plays a substantial role in forest ecosystem functioning. However, the amount and dynamics of dead wood in the forests of Northern Eurasia are poorly understood. The aim of this study was to develop a spatially distributed modelling system (limited to the territories of the former Soviet Union) to assess the amount and structure of dead wood by its components (including snags, logs, stumps, and the dry branches of living trees) based on the most comprehensive database of field measurements to date. The system is intended to be used to assess the dead wood volume and the amount of dead wood in carbon units as part of the carbon budget calculation of forests at different scales. It is presented using multi-dimensional regression equations of dead wood expansion factors (DWEF)—the ratio of the dead wood component volume to the growing stock volume of the stands. The system can be also used for the accounting of dead wood stock and its dynamics in national greenhouse gas inventories and UNFCCC reporting. The system’s accuracy is satisfactory for the average level of disturbance regimes but it may require corrections for regions with accelerated disturbance regimes

Climate-driven phenological changes in the Russian Arctic derived from MODIS LAI time series 2000–2019

Abstract: Arctic surface temperature has increased at approximately twice the global rate over the past few decades and is also projected to warm most in the 21st century. However, the mechanism of Arctic vegetation response to this warming remains largely uncertain. Here, we analyse variations in the seasonal profiles of MODerate resolution Imaging Spectroradiometer Leaf Area Index (LAI) and ERA-interim cumulative near-Surface Air Temperature (SATΣ) over the northern Russia, north of 60° N for 2000–2019. We find that commonly used broad temporal interval (seasonal) trends cannot fully represent complex interannual variations of the LAI profile over the growing season. A sequence of narrow temporal interval (weekly) LAI trends form an inverted S-shape over the course of the growing season with enhanced green-up and senescence, but balanced during the growing season’s peak. Spatial patterns of weekly LAI trends match with those of weekly SATΣ trends during the green-up, while the drivers of the browning trends during senescence remain unclear. Geographically the area with the statistically significant temperature-driven enhanced green-up is restricted by a large patch carrying significant positive SATΣ trends, which includes North Siberian Lowland, Taimyr, Yamal and adjacent territories. The strength, duration and timing of the changes depend on vegetation type: enhanced green-up is most pronounced in tundra, while enhanced senescence is pronounced in forests. Continued release of the climatic constraints will likely increase the capacity both of the environment (i.e. permafrost thawing) and vegetation (i.e. appearance of more productive woody species), and transform LAI seasonal shifts to change of LAI seasonal amplitude

Harmonisation, Mosaicing and Production of the Global Land Cover 2000 Database.

Abstract not availableJRC.H-Institute for environment and sustainability (Ispra

Global forest management data for 2015 at a 100 m resolution

Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki (https://www.geo-wiki.org/). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services

Global forest management data for 2015 at a 100 m resolution

Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki (https://www.geo-wiki.org/). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services